Название: Mean Field Guided Machine LearningАвтор: Yuhan Kang, Hao Gao, Zhu Han
Издательство: Springer
Год: 2025
Страниц: 159
Язык: английский
Формат: pdf (true), epub
b]Размер[/b]: 17.45 MB
This book explores the integration of Mean Field Game (MFG) theory with Machine Learning (ML), presenting both theoretical foundations and practical applications. Drawing from extensive research, it provides insights into how MFG can improve various ML techniques, including Supervised Learning, Reinforcement Learning, and Federated Learning. In this book, we explore the intersection of Mean Field Game (MFG) theory and Machine Learning, an innovative approach that holds promise for addressing these pressing challenges. MFG theory, which models the collective behavior of large populations of interacting agents, provides powerful mathematical tools for simplifying and optimizing complex systems. By integrating MFG theory with ML, we can enhance the efficiency, scalability, and robustness of Machine Learning models, opening new avenues for research and application development. With case studies and real-world examples, this book serves as a guide for researchers and students in communications and networks seeking to harness MFG’s potential in advancing ML.
